Overview

Dataset statistics

Number of variables14
Number of observations1404
Missing cells2691
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory153.7 KiB
Average record size in memory112.1 B

Variable types

Numeric11
Categorical3

Alerts

Geo_Desc has a high cardinality: 52 distinct valuesHigh cardinality
Year is highly overall correlated with Opioid_Prscrbng_RateHigh correlation
Geo_Cd is highly overall correlated with Geo_Lvl and 1 other fieldsHigh correlation
Tot_Opioid_Clms is highly overall correlated with Tot_Clms and 2 other fieldsHigh correlation
Tot_Clms is highly overall correlated with Tot_Opioid_Clms and 2 other fieldsHigh correlation
Opioid_Prscrbng_Rate is highly overall correlated with YearHigh correlation
LA_Tot_Opioid_Clms is highly overall correlated with Tot_Opioid_Clms and 2 other fieldsHigh correlation
LA_Opioid_Prscrbng_Rate is highly overall correlated with LA_Opioid_Prscrbng_Rate_5Y_ChgHigh correlation
LA_Opioid_Prscrbng_Rate_5Y_Chg is highly overall correlated with LA_Opioid_Prscrbng_RateHigh correlation
Geo_Lvl is highly overall correlated with Geo_Cd and 4 other fieldsHigh correlation
Geo_Desc is highly overall correlated with Geo_Cd and 1 other fieldsHigh correlation
Geo_Lvl is highly imbalanced (86.3%)Imbalance
Geo_Cd has 27 (1.9%) missing valuesMissing
Tot_Opioid_Clms has 18 (1.3%) missing valuesMissing
Opioid_Prscrbng_Rate has 132 (9.4%) missing valuesMissing
Opioid_Prscrbng_Rate_5Y_Chg has 852 (60.7%) missing valuesMissing
Opioid_Prscrbng_Rate_1Y_Chg has 285 (20.3%) missing valuesMissing
LA_Tot_Opioid_Clms has 36 (2.6%) missing valuesMissing
LA_Opioid_Prscrbng_Rate has 156 (11.1%) missing valuesMissing
LA_Opioid_Prscrbng_Rate_5Y_Chg has 872 (62.1%) missing valuesMissing
LA_Opioid_Prscrbng_Rate_1Y_Chg has 311 (22.2%) missing valuesMissing
Geo_Desc is uniformly distributedUniform
Plan_Type is uniformly distributedUniform
Tot_Opioid_Clms has 120 (8.5%) zerosZeros
Tot_Clms has 114 (8.1%) zerosZeros
LA_Tot_Opioid_Clms has 130 (9.3%) zerosZeros

Reproduction

Analysis started2023-09-19 14:02:59.558407
Analysis finished2023-09-19 14:03:42.566292
Duration43.01 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017
Minimum2013
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:42.780156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12015
median2017
Q32019
95-th percentile2021
Maximum2021
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5829089
Coefficient of variation (CV)0.0012805696
Kurtosis-1.2301059
Mean2017
Median Absolute Deviation (MAD)2
Skewness0
Sum2831868
Variance6.6714184
MonotonicityDecreasing
2023-09-19T14:03:43.138772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 156
11.1%
2020 156
11.1%
2019 156
11.1%
2018 156
11.1%
2017 156
11.1%
2016 156
11.1%
2015 156
11.1%
2014 156
11.1%
2013 156
11.1%
ValueCountFrequency (%)
2013 156
11.1%
2014 156
11.1%
2015 156
11.1%
2016 156
11.1%
2017 156
11.1%
2018 156
11.1%
2019 156
11.1%
2020 156
11.1%
2021 156
11.1%
ValueCountFrequency (%)
2021 156
11.1%
2020 156
11.1%
2019 156
11.1%
2018 156
11.1%
2017 156
11.1%
2016 156
11.1%
2015 156
11.1%
2014 156
11.1%
2013 156
11.1%

Geo_Lvl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
State
1377 
National
 
27

Length

Max length8
Median length5
Mean length5.0576923
Min length5

Characters and Unicode

Total characters7101
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowNational
3rd rowNational
4th rowState
5th rowState

Common Values

ValueCountFrequency (%)
State 1377
98.1%
National 27
 
1.9%

Length

2023-09-19T14:03:43.613072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T14:03:43.976321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
state 1377
98.1%
national 27
 
1.9%

Most occurring characters

ValueCountFrequency (%)
t 2781
39.2%
a 1431
20.2%
S 1377
19.4%
e 1377
19.4%
N 27
 
0.4%
i 27
 
0.4%
o 27
 
0.4%
n 27
 
0.4%
l 27
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5697
80.2%
Uppercase Letter 1404
 
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 2781
48.8%
a 1431
25.1%
e 1377
24.2%
i 27
 
0.5%
o 27
 
0.5%
n 27
 
0.5%
l 27
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1377
98.1%
N 27
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 7101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 2781
39.2%
a 1431
20.2%
S 1377
19.4%
e 1377
19.4%
N 27
 
0.4%
i 27
 
0.4%
o 27
 
0.4%
n 27
 
0.4%
l 27
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 2781
39.2%
a 1431
20.2%
S 1377
19.4%
e 1377
19.4%
N 27
 
0.4%
i 27
 
0.4%
o 27
 
0.4%
n 27
 
0.4%
l 27
 
0.4%

Geo_Cd
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)3.7%
Missing27
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean28.960784
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:44.433589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median29
Q342
95-th percentile54
Maximum56
Range55
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.682531
Coefficient of variation (CV)0.54150919
Kurtosis-1.1044434
Mean28.960784
Median Absolute Deviation (MAD)13
Skewness-0.01930637
Sum39879
Variance245.94177
MonotonicityNot monotonic
2023-09-19T14:03:44.788653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 27
 
1.9%
31 27
 
1.9%
32 27
 
1.9%
33 27
 
1.9%
34 27
 
1.9%
35 27
 
1.9%
36 27
 
1.9%
37 27
 
1.9%
38 27
 
1.9%
39 27
 
1.9%
Other values (41) 1107
78.8%
ValueCountFrequency (%)
1 27
1.9%
2 27
1.9%
4 27
1.9%
5 27
1.9%
6 27
1.9%
8 27
1.9%
9 27
1.9%
10 27
1.9%
11 27
1.9%
12 27
1.9%
ValueCountFrequency (%)
56 27
1.9%
55 27
1.9%
54 27
1.9%
53 27
1.9%
51 27
1.9%
50 27
1.9%
49 27
1.9%
48 27
1.9%
47 27
1.9%
46 27
1.9%

Geo_Desc
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM 

Distinct52
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
National
 
27
Alabama
 
27
Nebraska
 
27
Nevada
 
27
New Hampshire
 
27
Other values (47)
1269 

Length

Max length20
Median length12.5
Mean length8.6538462
Min length4

Characters and Unicode

Total characters12150
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowNational
3rd rowNational
4th rowAlabama
5th rowAlabama

Common Values

ValueCountFrequency (%)
National 27
 
1.9%
Alabama 27
 
1.9%
Nebraska 27
 
1.9%
Nevada 27
 
1.9%
New Hampshire 27
 
1.9%
New Jersey 27
 
1.9%
New Mexico 27
 
1.9%
New York 27
 
1.9%
North Carolina 27
 
1.9%
North Dakota 27
 
1.9%
Other values (42) 1134
80.8%

Length

2023-09-19T14:03:45.102193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
new 108
 
6.2%
carolina 54
 
3.1%
virginia 54
 
3.1%
dakota 54
 
3.1%
south 54
 
3.1%
north 54
 
3.1%
national 27
 
1.6%
florida 27
 
1.6%
columbia 27
 
1.6%
of 27
 
1.6%
Other values (46) 1242
71.9%

Most occurring characters

ValueCountFrequency (%)
a 1620
13.3%
i 1161
 
9.6%
o 972
 
8.0%
n 972
 
8.0%
s 837
 
6.9%
e 756
 
6.2%
r 594
 
4.9%
t 540
 
4.4%
l 432
 
3.6%
h 351
 
2.9%
Other values (36) 3915
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10125
83.3%
Uppercase Letter 1701
 
14.0%
Space Separator 324
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1620
16.0%
i 1161
11.5%
o 972
9.6%
n 972
9.6%
s 837
8.3%
e 756
 
7.5%
r 594
 
5.9%
t 540
 
5.3%
l 432
 
4.3%
h 351
 
3.5%
Other values (14) 1890
18.7%
Uppercase Letter
ValueCountFrequency (%)
N 243
14.3%
M 243
14.3%
C 162
9.5%
I 135
 
7.9%
W 108
 
6.3%
A 108
 
6.3%
D 108
 
6.3%
V 81
 
4.8%
O 81
 
4.8%
K 54
 
3.2%
Other values (11) 378
22.2%
Space Separator
ValueCountFrequency (%)
324
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11826
97.3%
Common 324
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1620
13.7%
i 1161
 
9.8%
o 972
 
8.2%
n 972
 
8.2%
s 837
 
7.1%
e 756
 
6.4%
r 594
 
5.0%
t 540
 
4.6%
l 432
 
3.7%
h 351
 
3.0%
Other values (35) 3591
30.4%
Common
ValueCountFrequency (%)
324
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1620
13.3%
i 1161
 
9.6%
o 972
 
8.0%
n 972
 
8.0%
s 837
 
6.9%
e 756
 
6.2%
r 594
 
4.9%
t 540
 
4.4%
l 432
 
3.6%
h 351
 
2.9%
Other values (36) 3915
32.2%

Plan_Type
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
All
468 
FFS
468 
MC
468 

Length

Max length3
Median length3
Mean length2.6666667
Min length2

Characters and Unicode

Total characters3744
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowFFS
3rd rowMC
4th rowAll
5th rowFFS

Common Values

ValueCountFrequency (%)
All 468
33.3%
FFS 468
33.3%
MC 468
33.3%

Length

2023-09-19T14:03:45.355447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-19T14:03:45.613372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
all 468
33.3%
ffs 468
33.3%
mc 468
33.3%

Most occurring characters

ValueCountFrequency (%)
l 936
25.0%
F 936
25.0%
A 468
12.5%
S 468
12.5%
M 468
12.5%
C 468
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2808
75.0%
Lowercase Letter 936
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 936
33.3%
A 468
16.7%
S 468
16.7%
M 468
16.7%
C 468
16.7%
Lowercase Letter
ValueCountFrequency (%)
l 936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3744
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 936
25.0%
F 936
25.0%
A 468
12.5%
S 468
12.5%
M 468
12.5%
C 468
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 936
25.0%
F 936
25.0%
A 468
12.5%
S 468
12.5%
M 468
12.5%
C 468
12.5%

Tot_Opioid_Clms
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1147
Distinct (%)82.8%
Missing18
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean770834.79
Minimum0
Maximum37964067
Zeros120
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:45.897364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141961.25
median182083
Q3570217.25
95-th percentile1782211.2
Maximum37964067
Range37964067
Interquartile range (IQR)528256

Descriptive statistics

Standard deviation3049433.7
Coefficient of variation (CV)3.9560147
Kurtosis81.357535
Mean770834.79
Median Absolute Deviation (MAD)178277.5
Skewness8.5753739
Sum1.068377 × 109
Variance9.2990461 × 1012
MonotonicityNot monotonic
2023-09-19T14:03:46.187593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
8.5%
447039 2
 
0.1%
613637 2
 
0.1%
1017833 2
 
0.1%
49072 2
 
0.1%
645729 2
 
0.1%
150780 2
 
0.1%
132076 2
 
0.1%
507923 2
 
0.1%
1192423 2
 
0.1%
Other values (1137) 1248
88.9%
(Missing) 18
 
1.3%
ValueCountFrequency (%)
0 120
8.5%
12 1
 
0.1%
13 1
 
0.1%
24 1
 
0.1%
32 1
 
0.1%
47 1
 
0.1%
50 1
 
0.1%
62 1
 
0.1%
130 1
 
0.1%
161 1
 
0.1%
ValueCountFrequency (%)
37964067 1
0.1%
37014898 1
0.1%
36902273 1
0.1%
33471552 1
0.1%
32240821 1
0.1%
25910488 1
0.1%
25287108 1
0.1%
24960573 1
0.1%
23122001 1
0.1%
22058928 1
0.1%

Tot_Clms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1175
Distinct (%)83.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean16592302
Minimum0
Maximum7.0429677 × 108
Zeros114
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:46.483513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11096489.2
median4402720
Q311378235
95-th percentile38677654
Maximum7.0429677 × 108
Range7.0429677 × 108
Interquartile range (IQR)10281746

Descriptive statistics

Standard deviation64943294
Coefficient of variation (CV)3.9140618
Kurtosis71.562763
Mean16592302
Median Absolute Deviation (MAD)3989145.5
Skewness8.173525
Sum2.3262407 × 1010
Variance4.2176314 × 1015
MonotonicityNot monotonic
2023-09-19T14:03:46.774209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 114
 
8.1%
529696 2
 
0.1%
4700499 2
 
0.1%
10569244 2
 
0.1%
7143629 2
 
0.1%
8285804 2
 
0.1%
442330 2
 
0.1%
7319465 2
 
0.1%
1396466 2
 
0.1%
7584452 2
 
0.1%
Other values (1165) 1270
90.5%
ValueCountFrequency (%)
0 114
8.1%
576 1
 
0.1%
2147 1
 
0.1%
3884 1
 
0.1%
4542 1
 
0.1%
4891 1
 
0.1%
5905 1
 
0.1%
6114 1
 
0.1%
7857 1
 
0.1%
8746 1
 
0.1%
ValueCountFrequency (%)
704296772 1
0.1%
686625295 1
0.1%
685892618 1
0.1%
685821751 1
0.1%
679767097 1
0.1%
653713145 1
0.1%
641988059 1
0.1%
571877738 1
0.1%
508189967 1
0.1%
506882862 1
0.1%

Opioid_Prscrbng_Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct664
Distinct (%)52.2%
Missing132
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean5.0143396
Minimum0
Maximum29.44
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:47.097419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4955
Q13.09
median4.64
Q36.42
95-th percentile9.5925
Maximum29.44
Range29.44
Interquartile range (IQR)3.33

Descriptive statistics

Standard deviation2.783966
Coefficient of variation (CV)0.55520093
Kurtosis8.7015997
Mean5.0143396
Median Absolute Deviation (MAD)1.69
Skewness1.8186794
Sum6378.24
Variance7.7504669
MonotonicityNot monotonic
2023-09-19T14:03:47.398875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 7
 
0.5%
4.18 7
 
0.5%
5.55 7
 
0.5%
0 6
 
0.4%
3.18 6
 
0.4%
4.4 6
 
0.4%
5.76 6
 
0.4%
2.95 6
 
0.4%
2.75 6
 
0.4%
4.88 6
 
0.4%
Other values (654) 1209
86.1%
(Missing) 132
 
9.4%
ValueCountFrequency (%)
0 6
0.4%
0.02 1
 
0.1%
0.03 1
 
0.1%
0.04 1
 
0.1%
0.05 1
 
0.1%
0.15 1
 
0.1%
0.23 1
 
0.1%
0.29 1
 
0.1%
0.35 1
 
0.1%
0.38 1
 
0.1%
ValueCountFrequency (%)
29.44 1
0.1%
24.2 1
0.1%
20.1 1
0.1%
19.68 1
0.1%
18.49 1
0.1%
18.36 1
0.1%
17.78 1
0.1%
16.19 1
0.1%
16 1
0.1%
15.36 1
0.1%
Distinct330
Distinct (%)59.8%
Missing852
Missing (%)60.7%
Infinite0
Infinite (%)0.0%
Mean-2.8291486
Minimum-10.42
Maximum16.19
Zeros0
Zeros (%)0.0%
Negative520
Negative (%)37.0%
Memory size11.1 KiB
2023-09-19T14:03:47.688127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-10.42
5-th percentile-5.716
Q1-3.91
median-3.065
Q3-2.2375
95-th percentile0.8525
Maximum16.19
Range26.61
Interquartile range (IQR)1.6725

Descriptive statistics

Standard deviation2.4213161
Coefficient of variation (CV)-0.85584624
Kurtosis15.870123
Mean-2.8291486
Median Absolute Deviation (MAD)0.845
Skewness2.7550462
Sum-1561.69
Variance5.8627719
MonotonicityNot monotonic
2023-09-19T14:03:47.994926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.92 7
 
0.5%
-3.33 6
 
0.4%
-3.37 6
 
0.4%
-3.76 5
 
0.4%
-2.88 5
 
0.4%
-2.33 5
 
0.4%
-2.15 5
 
0.4%
-2.44 5
 
0.4%
-3.07 5
 
0.4%
-1.13 5
 
0.4%
Other values (320) 498
35.5%
(Missing) 852
60.7%
ValueCountFrequency (%)
-10.42 1
0.1%
-9.91 1
0.1%
-9.48 1
0.1%
-9.21 1
0.1%
-8.67 1
0.1%
-7.64 1
0.1%
-7.46 1
0.1%
-7.32 1
0.1%
-7 1
0.1%
-6.83 1
0.1%
ValueCountFrequency (%)
16.19 1
0.1%
13.84 1
0.1%
10.86 1
0.1%
8.99 1
0.1%
8.9 1
0.1%
7.12 1
0.1%
6.65 1
0.1%
6.45 1
0.1%
5.93 1
0.1%
5.7 1
0.1%
Distinct258
Distinct (%)23.1%
Missing285
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean-0.44741734
Minimum-4.1
Maximum15.31
Zeros4
Zeros (%)0.3%
Negative982
Negative (%)69.9%
Memory size11.1 KiB
2023-09-19T14:03:48.278980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.1
5-th percentile-1.431
Q1-0.82
median-0.48
Q3-0.18
95-th percentile0.351
Maximum15.31
Range19.41
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation1.0387954
Coefficient of variation (CV)-2.3217594
Kurtosis86.899307
Mean-0.44741734
Median Absolute Deviation (MAD)0.31
Skewness6.9363108
Sum-500.66
Variance1.0790959
MonotonicityNot monotonic
2023-09-19T14:03:48.605819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.39 18
 
1.3%
-0.67 16
 
1.1%
-0.17 16
 
1.1%
-0.51 15
 
1.1%
-0.56 15
 
1.1%
-0.28 14
 
1.0%
-0.48 14
 
1.0%
-0.18 14
 
1.0%
-0.26 13
 
0.9%
-0.13 13
 
0.9%
Other values (248) 971
69.2%
(Missing) 285
 
20.3%
ValueCountFrequency (%)
-4.1 1
0.1%
-3.5 1
0.1%
-3.49 1
0.1%
-3.47 1
0.1%
-3.4 1
0.1%
-3.18 1
0.1%
-2.93 1
0.1%
-2.86 1
0.1%
-2.84 1
0.1%
-2.77 1
0.1%
ValueCountFrequency (%)
15.31 1
0.1%
13.25 1
0.1%
9.06 1
0.1%
7.02 1
0.1%
7 1
0.1%
6.36 1
0.1%
4.26 1
0.1%
4.01 1
0.1%
3.79 1
0.1%
2.78 2
0.1%

LA_Tot_Opioid_Clms
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1094
Distinct (%)80.0%
Missing36
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean80656.536
Minimum0
Maximum4672903
Zeros130
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:48.902577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13646
median14459
Q346538
95-th percentile150874.65
Maximum4672903
Range4672903
Interquartile range (IQR)42892

Descriptive statistics

Standard deviation347495.96
Coefficient of variation (CV)4.3083422
Kurtosis73.86094
Mean80656.536
Median Absolute Deviation (MAD)13613
Skewness8.0951964
Sum1.1033814 × 108
Variance1.2075344 × 1011
MonotonicityNot monotonic
2023-09-19T14:03:49.209915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
9.3%
12207 3
 
0.2%
4230 2
 
0.1%
30850 2
 
0.1%
8657 2
 
0.1%
22998 2
 
0.1%
15295 2
 
0.1%
65204 2
 
0.1%
53120 2
 
0.1%
10307 2
 
0.1%
Other values (1084) 1219
86.8%
(Missing) 36
 
2.6%
ValueCountFrequency (%)
0 130
9.3%
11 1
 
0.1%
16 2
 
0.1%
22 1
 
0.1%
23 1
 
0.1%
31 1
 
0.1%
37 2
 
0.1%
45 1
 
0.1%
49 2
 
0.1%
50 1
 
0.1%
ValueCountFrequency (%)
4672903 1
0.1%
4008483 1
0.1%
3936336 1
0.1%
3296403 1
0.1%
3119928 1
0.1%
2885869 1
0.1%
2783521 1
0.1%
2767969 1
0.1%
2605496 1
0.1%
2461048 1
0.1%

LA_Opioid_Prscrbng_Rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct749
Distinct (%)60.0%
Missing156
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean10.049792
Minimum0
Maximum97.47
Zeros10
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-09-19T14:03:49.501415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8075
Q15.5975
median8.25
Q310.355
95-th percentile19.661
Maximum97.47
Range97.47
Interquartile range (IQR)4.7575

Descriptive statistics

Standard deviation10.934707
Coefficient of variation (CV)1.0880531
Kurtosis30.185258
Mean10.049792
Median Absolute Deviation (MAD)2.395
Skewness5.1095367
Sum12542.14
Variance119.56782
MonotonicityNot monotonic
2023-09-19T14:03:50.175404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.7%
7.88 7
 
0.5%
9.16 6
 
0.4%
7.93 6
 
0.4%
8 6
 
0.4%
11.59 5
 
0.4%
3.05 5
 
0.4%
6.06 5
 
0.4%
4.79 5
 
0.4%
7.16 5
 
0.4%
Other values (739) 1188
84.6%
(Missing) 156
 
11.1%
ValueCountFrequency (%)
0 10
0.7%
0.58 1
 
0.1%
1.15 1
 
0.1%
1.52 1
 
0.1%
1.55 1
 
0.1%
1.79 1
 
0.1%
1.82 1
 
0.1%
1.85 1
 
0.1%
1.91 1
 
0.1%
1.95 1
 
0.1%
ValueCountFrequency (%)
97.47 1
0.1%
94.17 1
0.1%
91.93 1
0.1%
91.7 1
0.1%
91.31 1
0.1%
87.97 1
0.1%
87.93 1
0.1%
85.95 2
0.1%
85.81 1
0.1%
80.45 1
0.1%

LA_Opioid_Prscrbng_Rate_5Y_Chg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct377
Distinct (%)70.9%
Missing872
Missing (%)62.1%
Infinite0
Infinite (%)0.0%
Mean3.0459398
Minimum-14.26
Maximum84.25
Zeros1
Zeros (%)0.1%
Negative295
Negative (%)21.0%
Memory size11.1 KiB
2023-09-19T14:03:50.465825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14.26
5-th percentile-4.4485
Q1-1.5
median-0.22
Q31.225
95-th percentile38.52
Maximum84.25
Range98.51
Interquartile range (IQR)2.725

Descriptive statistics

Standard deviation14.251594
Coefficient of variation (CV)4.6788822
Kurtosis15.545047
Mean3.0459398
Median Absolute Deviation (MAD)1.365
Skewness3.9421733
Sum1620.44
Variance203.10793
MonotonicityNot monotonic
2023-09-19T14:03:50.755732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.31 6
 
0.4%
1.03 5
 
0.4%
-0.36 5
 
0.4%
-0.17 5
 
0.4%
0.13 4
 
0.3%
-0.85 4
 
0.3%
-0.22 4
 
0.3%
-1.6 3
 
0.2%
-0.14 3
 
0.2%
-3.09 3
 
0.2%
Other values (367) 490
34.9%
(Missing) 872
62.1%
ValueCountFrequency (%)
-14.26 1
0.1%
-12.55 1
0.1%
-10.85 1
0.1%
-10.61 1
0.1%
-9.74 1
0.1%
-9.62 1
0.1%
-8.38 1
0.1%
-8.01 1
0.1%
-7.97 1
0.1%
-7.79 1
0.1%
ValueCountFrequency (%)
84.25 1
0.1%
81.97 1
0.1%
81.07 1
0.1%
78.69 1
0.1%
78.57 1
0.1%
75.64 1
0.1%
74.1 1
0.1%
72.93 1
0.1%
72.56 1
0.1%
62.57 1
0.1%
Distinct399
Distinct (%)36.5%
Missing311
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean0.55970723
Minimum-12.33
Maximum92.65
Zeros10
Zeros (%)0.7%
Negative576
Negative (%)41.0%
Memory size11.1 KiB
2023-09-19T14:03:51.057825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-12.33
5-th percentile-1.93
Q1-0.57
median-0.06
Q30.46
95-th percentile2.974
Maximum92.65
Range104.98
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation5.1390245
Coefficient of variation (CV)9.1816297
Kurtosis126.59373
Mean0.55970723
Median Absolute Deviation (MAD)0.52
Skewness9.5944777
Sum611.76
Variance26.409573
MonotonicityNot monotonic
2023-09-19T14:03:51.338892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18 12
 
0.9%
0.01 10
 
0.7%
-0.29 10
 
0.7%
-0.06 10
 
0.7%
0 10
 
0.7%
-0.12 9
 
0.6%
-0.4 9
 
0.6%
0.13 9
 
0.6%
-0.28 9
 
0.6%
-0.11 9
 
0.6%
Other values (389) 996
70.9%
(Missing) 311
 
22.2%
ValueCountFrequency (%)
-12.33 1
0.1%
-12.31 1
0.1%
-8.01 1
0.1%
-7.96 1
0.1%
-6.59 1
0.1%
-5.86 1
0.1%
-5.78 1
0.1%
-5.74 1
0.1%
-5.64 1
0.1%
-5.14 1
0.1%
ValueCountFrequency (%)
92.65 1
0.1%
50.32 1
0.1%
47.25 1
0.1%
45.81 1
0.1%
40.49 1
0.1%
40.35 1
0.1%
38.68 2
0.1%
24.63 1
0.1%
24.56 1
0.1%
23.6 1
0.1%

Interactions

2023-09-19T14:03:35.835692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:02.340120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:05.569194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:08.235385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:11.158321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:14.395154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:17.932519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:20.774670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:23.586610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:26.367737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:31.929491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:36.100528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:02.738695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:05.826225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:08.499722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:11.414963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:14.782198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:18.175198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:21.057884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:23.844908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:26.636469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:32.240266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:36.358859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:03.084155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:06.053872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:08.740351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:11.662197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:15.106577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:18.412850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:21.318519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:24.102797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:26.884929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:32.482467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:36.653348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:03.504669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:06.275590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:08.999411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:11.937772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:15.521732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:18.660772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:21.589045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:24.359163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:27.280881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:32.735508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:37.087147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:03.779929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:06.517991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:09.256922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:12.189439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:15.901750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:18.904583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:21.854664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:24.609531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:27.968390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:33.104400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:37.486120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:04.039365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:06.760251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:09.503715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:12.432873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:16.256409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:19.152177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:22.117576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:24.873694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:28.703020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:33.450534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:37.769356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:04.293549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:06.999085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:09.745246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:12.667224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:16.606453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:19.399327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:22.359363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:25.131754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:29.304343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:33.850568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:38.073268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:04.586989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:07.268277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:10.015052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:12.949133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:16.948349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:19.649886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:22.630882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:25.391594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:30.183060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:34.824516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:38.337136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:04.831061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:07.522220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:10.427895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:13.269211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:17.185476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:20.117229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:22.864662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:25.638585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:30.599682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:35.087290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:38.633676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:05.081484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:07.770811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:10.693460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:13.668992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:17.430041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:20.331824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:23.123576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:25.889744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:31.008677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:35.320984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:38.912727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:05.314808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:08.009878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:10.920597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:14.033792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:17.685208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:20.555290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:23.344100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:26.133246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:31.396191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-19T14:03:35.576749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-19T14:03:51.578252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearGeo_CdTot_Opioid_ClmsTot_ClmsOpioid_Prscrbng_RateOpioid_Prscrbng_Rate_5Y_ChgOpioid_Prscrbng_Rate_1Y_ChgLA_Tot_Opioid_ClmsLA_Opioid_Prscrbng_RateLA_Opioid_Prscrbng_Rate_5Y_ChgLA_Opioid_Prscrbng_Rate_1Y_ChgGeo_LvlGeo_DescPlan_Type
Year1.0000.000-0.1340.024-0.6180.1440.177-0.123-0.026-0.111-0.1130.0000.0000.000
Geo_Cd0.0001.000-0.033-0.0430.002-0.052-0.0350.0100.1660.2070.0771.0000.9850.000
Tot_Opioid_Clms-0.134-0.0331.0000.9560.3060.013-0.0250.945-0.1360.1350.1090.9590.2970.077
Tot_Clms0.024-0.0430.9561.0000.0180.0310.0130.883-0.1870.0670.0620.9970.3220.122
Opioid_Prscrbng_Rate-0.6180.0020.3060.0181.000-0.150-0.1370.3780.1430.2540.1640.0380.2540.149
Opioid_Prscrbng_Rate_5Y_Chg0.144-0.0520.0130.031-0.1501.0000.4320.0430.1150.1300.0980.0000.4300.141
Opioid_Prscrbng_Rate_1Y_Chg0.177-0.035-0.0250.013-0.1370.4321.0000.0000.0650.086-0.0420.0000.1570.080
LA_Tot_Opioid_Clms-0.1230.0100.9450.8830.3780.0430.0001.0000.1760.3350.2100.9230.2600.091
LA_Opioid_Prscrbng_Rate-0.0260.166-0.136-0.1870.1430.1150.0650.1761.0000.5780.2770.0460.4060.096
LA_Opioid_Prscrbng_Rate_5Y_Chg-0.1110.2070.1350.0670.2540.1300.0860.3350.5781.0000.4860.3280.4470.059
LA_Opioid_Prscrbng_Rate_1Y_Chg-0.1130.0770.1090.0620.1640.098-0.0420.2100.2770.4861.0000.0000.2600.101
Geo_Lvl0.0001.0000.9590.9970.0380.0000.0000.9230.0460.3280.0001.0000.9820.000
Geo_Desc0.0000.9850.2970.3220.2540.4300.1570.2600.4060.4470.2600.9821.0000.000
Plan_Type0.0000.0000.0770.1220.1490.1410.0800.0910.0960.0590.1010.0000.0001.000

Missing values

2023-09-19T14:03:39.289116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-19T14:03:40.451266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-19T14:03:41.610663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

YearGeo_LvlGeo_CdGeo_DescPlan_TypeTot_Opioid_ClmsTot_ClmsOpioid_Prscrbng_RateOpioid_Prscrbng_Rate_5Y_ChgOpioid_Prscrbng_Rate_1Y_ChgLA_Tot_Opioid_ClmsLA_Opioid_Prscrbng_RateLA_Opioid_Prscrbng_Rate_5Y_ChgLA_Opioid_Prscrbng_Rate_1Y_Chg
02021NationalNaNNationalAll21654225.0686625295.03.15-2.23-0.094672903.021.5814.042.64
12021NationalNaNNationalFFS5084859.0180712324.02.81-2.48-0.15736567.014.495.341.05
22021NationalNaNNationalMC16569366.0505912971.03.28-2.14-0.063936336.023.7616.902.98
32021State1.0AlabamaAll175237.07525456.02.33-3.04-0.387767.04.43-0.36-0.18
42021State1.0AlabamaFFS175237.07525456.02.33-3.04-0.387767.04.43-0.36-0.18
52021State1.0AlabamaMC0.00.0NaNNaNNaN0.0NaNNaNNaN
62021State2.0AlaskaAll58330.01436383.04.06-3.84-0.247076.012.130.54-0.58
72021State2.0AlaskaFFS58330.01436383.04.06-3.84-0.247076.012.130.54-0.58
82021State2.0AlaskaMC0.00.0NaNNaNNaN0.0NaNNaNNaN
92021State4.0ArizonaAll512306.014333371.03.57-3.72-0.1032317.06.31-4.52-0.90
YearGeo_LvlGeo_CdGeo_DescPlan_TypeTot_Opioid_ClmsTot_ClmsOpioid_Prscrbng_RateOpioid_Prscrbng_Rate_5Y_ChgOpioid_Prscrbng_Rate_1Y_ChgLA_Tot_Opioid_ClmsLA_Opioid_Prscrbng_RateLA_Opioid_Prscrbng_Rate_5Y_ChgLA_Opioid_Prscrbng_Rate_1Y_Chg
13942013State53.0WashingtonMC431779.04637269.09.31NaNNaN45745.010.59NaNNaN
13952013State54.0West VirginiaAll401118.05794848.06.92NaNNaN22227.05.54NaNNaN
13962013State54.0West VirginiaFFS329579.04721906.06.98NaNNaN20429.06.20NaNNaN
13972013State54.0West VirginiaMC71539.01072942.06.67NaNNaN1798.02.51NaNNaN
13982013State55.0WisconsinAll899000.010155576.08.85NaNNaN120342.013.39NaNNaN
13992013State55.0WisconsinFFS896650.010127535.08.85NaNNaN119908.013.37NaNNaN
14002013State55.0WisconsinMC2350.028041.08.38NaNNaN434.018.47NaNNaN
14012013State56.0WyomingAll41202.0529696.07.78NaNNaN4230.010.27NaNNaN
14022013State56.0WyomingFFS41202.0529696.07.78NaNNaN4230.010.27NaNNaN
14032013State56.0WyomingMC0.00.0NaNNaNNaN0.0NaNNaNNaN